Positioning of objects (i.e., vehicles, buildings, pedestrians, etc.) can be determined using GPS (Global Positioning System) or GNSS (Global Navigation Satellite System). Some applications that use positioning, such as assisted driving, need high precision data to be implemented safely. To calculate high precision data with GPS/GNSS, real-time kinematics (RTK) with base-stations are used, which is currently not viable for commercial use. Host vehicle gyros can also be used to estimate a future expected trajectory of objects, but if the initial GPS/GNSS position is incorrect the problem cannot be solved correctly.
In assisted driving applications, such as active intervention, knowledge of the positions of surrounding vehicles with high accuracy is utilized, which is currently implemented using many different sensors covering 360 degrees around a vehicle. There are many vehicle environment detection systems, such as camera systems, Doppler radar systems and LIDAR systems. Inaccuracies can lead to both false-positives and false-negatives. GPS/GNSS does not provide a sufficient degree of accuracy, especially in urban conditions.
Using periodic broadcasts to perform inter-vehicle range estimation can be used to obtain a high degree of accuracy regarding relative positioning between objects. Determining highly accurate relative positioning between objects introduces additional issues. One such issue is that communication is limited by an amount of transmit power. In clustering, the intended communication recipient vehicles are selected based on current traffic scenarios. In some traffic scenarios, parts of the desired clustering group are outside of the communication range of the host vehicle. Conventional vehicle-to-vehicle (i.e., V2V) communication solutions change the group size and members when entering the communication range. In high traffic scenarios, changing the group size and members limits the usefulness of communications.
It would be desirable to implement V2V clustering and multi-hop communication.